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Daniel Pamungkas
Politeknik Negeri Batam
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Face Recognition for an Attendance System Daniel Pamungkas; Frederik Mahardika
International ABEC 2021: Proceeding International Applied Business and Engineering Conference 2021
Publisher : International ABEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (806.392 KB)

Abstract

In the pandemic era, the attendance system needs to be contactless. Moreover, this system needs to be more automatic compare to the existing systems. This paper introduces the visual system using face recognition. The Haar cascade method and Local Binary pattern histogram algorithm is used to recognize the user’s identity. To capture the face, a webcam is used. This system enables detection and identifies the users. It also stores the time that check-in and checks out of the users automatically. The proposed system adequate to detect face up to 55 cm in the low lighting condition. Furthermore, this system enables to detection of multiple users in one frame.
Sistem Pengenalan Wajah dengan Algoritma Haar Cascade dan Local Binary Pattern Histogram Sayeed Al-Aidid; Daniel Pamungkas
Jurnal Rekayasa Elektrika Vol 14, No 1 (2018)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (708.205 KB) | DOI: 10.17529/jre.v14i1.9799

Abstract

Recently, the applications of face recognition are increasing significantly. Some methods have already been tried, but the results have not optimal yet. This paper tries to overcome this problem, using haar cascade as face detection algorithm, whereas face recognition uses local binary pattern histogram method. This system uses a webcam as a camera and programming exploit OpenCV library. This system enables to differentiate the face of the human with others objects with the best range from the camera to the object is 50 cm until 150 cm. In addition, this system is capable to recognize faces from the 6 subjects of faces listed in the database, alone and in a group as well in one frame.
Perbandingan Antara Domain Waktu dan Frekuensi untuk Pengenalan Sinyal EMG Daniel Pamungkas; Sumantri R Kurniawan; Benrico F Simamora
Jurnal Rekayasa Elektrika Vol 17, No 1 (2021)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1905.036 KB) | DOI: 10.17529/jre.v17i1.16844

Abstract

One way to recognize hand gestures is to use signal electromyography (EMG). The processed signal can use the time domain, frequency domain, or a mixture of the two domains. Meanwhile, the classification method that is widely used recently is the classification of Artificial Neural Networks (ANN). This paper presents a comparison study between time domains with frequency domain for EMG signals using ANN classification. This comparison aims to find out a better method for controlling the hand robot. The time domain features are root mean square (RMS) of the signal, while the signal’s octave band becomes a feature of the frequency domain. The EMG signals were obtained from the subject with eight fingers gestures. The results of this classification are used to control the robot’s hand. The success of each method in recognizing hand movements was counted. In addition, the response speed of the robot in changing positions is measured. The results showed that features using the frequency domain had a higher percentage of success than another domain. But the speed and memory used then the system using signals in the time domain is better.
Perbandingan Antara Domain Waktu dan Frekuensi untuk Pengenalan Sinyal EMG Daniel Pamungkas; Sumantri R Kurniawan; Benrico F Simamora
Jurnal Rekayasa Elektrika Vol 17, No 1 (2021)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v17i1.16844

Abstract

One way to recognize hand gestures is to use signal electromyography (EMG). The processed signal can use the time domain, frequency domain, or a mixture of the two domains. Meanwhile, the classification method that is widely used recently is the classification of Artificial Neural Networks (ANN). This paper presents a comparison study between time domains with frequency domain for EMG signals using ANN classification. This comparison aims to find out a better method for controlling the hand robot. The time domain features are root mean square (RMS) of the signal, while the signal’s octave band becomes a feature of the frequency domain. The EMG signals were obtained from the subject with eight fingers gestures. The results of this classification are used to control the robot’s hand. The success of each method in recognizing hand movements was counted. In addition, the response speed of the robot in changing positions is measured. The results showed that features using the frequency domain had a higher percentage of success than another domain. But the speed and memory used then the system using signals in the time domain is better.
Sistem Pengenalan Wajah dengan Algoritma Haar Cascade dan Local Binary Pattern Histogram Sayeed Al-Aidid; Daniel Pamungkas
Jurnal Rekayasa Elektrika Vol 14, No 1 (2018)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v14i1.9799

Abstract

Recently, the applications of face recognition are increasing significantly. Some methods have already been tried, but the results have not optimal yet. This paper tries to overcome this problem, using haar cascade as face detection algorithm, whereas face recognition uses local binary pattern histogram method. This system uses a webcam as a camera and programming exploit OpenCV library. This system enables to differentiate the face of the human with others objects with the best range from the camera to the object is 50 cm until 150 cm. In addition, this system is capable to recognize faces from the 6 subjects of faces listed in the database, alone and in a group as well in one frame.
Co-Authors Benrico F Simamora Frederik Mahardika Sayeed Al-Aidid Sumantri R Kurniawan